Dropout causes too much noise for network to train
why dropout of 0.5 effectively kills the networks ability to train.


because that is too mutch normal values are like 0.15-0.05. Imagine, the 50% of input image is randomly set to 0, THEN it happens on next layer, means in average 25%  remains, etc... also if you have small dataset with too different images for each class, this + drouput wil confuse the network. 
Also your CNN setup is not realy rational. Too much fc layers, replace one or two fc to convo layers.  And i d say the reason of using 4x4 is only with stride 2, else use 3x3. And you sould use batch normalisation and augmentation like small noice would be probably petter then dropout in your case.